Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions

Fluid antenna system (FAS) as a new version of reconfigurable antenna technologies promoting shape and position flexibility, has emerged as an exciting and possibly transformative technology for wireless communications systems. FAS represents any software-controlled fluidic, conductive or dielectric structure that can dynamically alter antenna's shape and position to change the gain, the radiation pattern, the operating frequency, and other critical radiation characteristics. With its capability, it is highly anticipated that FAS can contribute greatly to the upcoming sixth generation (6G) wireless networks. This article substantiates this thought by addressing four major questions: 1) Is FAS crucial to 6G? 2) How to characterize FAS? 3) What are the applications of FAS? 4) What are the relevant challenges and future research directions? In particular, five promising research directions that underscore the potential of FAS are discussed. We conclude this article by showcasing the impressive performance of FAS.

Fluid Antenna-Aided Rate-Splitting Multiple Access

This letter considers a fluid antenna system (FAS)-aided rate-splitting multiple access (RSMA) approach for downlink transmission. In particular, a base station (BS) equipped with a single traditional antenna system (TAS) uses RSMA signaling to send information to several mobile users (MUs) each equipped with FAS. To understand the achievable performance, we first present the distribution of the equivalent channel gain based on the joint multivariate t-distribution and then derive a compact analytical expression for the outage probability (OP). Moreover, we obtain the asymptotic OP in the high signal-to-noise ratio (SNR) regime. Numerical results show that combining FAS with RSMA significantly outperforms TAS and conventional multiple access schemes, such as non-orthogonal multiple access (NOMA), in terms of OP. The results also indicate that FAS can be the tool that greatly improves the practicality of RSMA.

FAS for Secure and Covert Communications

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is non-convex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas' positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes.

A Secure Beamforming Design: When Fluid Antenna Meets NOMA

This letter proposes a secure beamforming design for downlink non-orthogonal multiple access (NOMA) systems utilizing fluid antenna systems (FAS). We consider a setup where a base station (BS) with $M$ fluid antennas (FAs) communicates to a cell-center user (CU) and a cell-edge user (CEU), each with a FA. The CU is the intended recipient while the CEU is regarded as a potential eavesdropper. Our aim is to maximize the achievable secrecy rate by jointly optimizing the secure beamforming vectors and the positions of FAs. To tackle this, we adopt an alternating optimization (AO) algorithm that optimizes secure beamforming and the positions of the FAs iteratively while keeping the other variables fixed. Numerical results illustrate that when FAs meet NOMA, the proposed scheme greatly enhances the secrecy rate compared to conventional multiple-input single-output (MISO) fixed antenna NOMA systems and other benchmark schemes.

FAS-Driven Spectrum Sensing for Cognitive Radio Networks

Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FAS) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study a FAS-driven CR setup where a secondary user (SU) adjusts the positions of fluid antennas to detect signals from the primary user (PU). We aim to maximize the detection probability under the constraints of the false alarm probability and the received beamforming of the SU. To address this problem, we first derive a closed-form expression for the optimal detection threshold and reformulate the problem to find its solution. Then an alternating optimization (AO) scheme is proposed to decompose the problem into several sub-problems, addressing both the received beamforming and the antenna positions at the SU. The beamforming subproblem is addressed using a closed-form solution, while the fluid antenna positions are solved by successive convex approximation (SCA). Simulation results reveal that the proposed algorithm provides significant improvements over traditional fixed-position antenna (FPA) schemes in terms of spectrum sensing performance.

Antenna Coding Empowered by Pixel Antennas

Pixel antennas, based on discretizing a continuous radiation surface into small elements called pixels, are a flexible reconfigurable antenna technology. By controlling the connections between pixels via switches, the characteristics of pixel antennas can be adjusted to enhance the wireless channel. Inspired by this, we propose a novel technique denoted antenna coding empowered by pixel antennas. We first derive a physical and electromagnetic based communication model for pixel antennas using microwave multiport network theory and beamspace channel representation. With the model, we optimize the antenna coding to maximize the channel gain in a single-input single-output (SISO) pixel antenna system and develop a codebook design for antenna coding to reduce the computational complexity. We analyze the average channel gain of SISO pixel antenna system and derive the corresponding upper bound. In addition, we jointly optimize the antenna coding and transmit signal covariance matrix to maximize the channel capacity in a multiple-input multiple-output (MIMO) pixel antenna system. Simulation results show that using pixel antennas can enhance the average channel gain by up to 5.4 times and channel capacity by up to 3.1 times, demonstrating the significant potential of pixel antennas as a new dimension to design and optimize wireless communication systems.

Frequency Diverse RIS (FD-RIS) Enhanced Wireless Communications via Joint Distance-Angle Beamforming

The conventional reconfigurable intelligent surface (RIS) assisted far-field communication systems can only implement angle beamforming, which actually limits the capability for reconfiguring the wireless propagation environment. To overcome this limitation, this paper proposes a newly designed frequency diverse RIS (FD-RIS), which can achieve joint distance-angle beamforming with the assistance of the time modulation technology. The signal processing model for FD-RIS-aided wireless communications is first derived. Then, an optimization problem aimed at maximizing the achievable rate is formulated where the frequency-time modulations are jointly optimized to achieve distance-angle beamforming. Furthermore, a novel iterative algorithm based on the cross-entropy optimization (CEO) framework is proposed to effectively handle the non-convex optimization problem. The numerical results validate that the proposed FD-RIS assisted communication scheme can achieve a notable performance improvement compared with the baseline scheme utilizing traditional RIS. In addition, the effectiveness of the proposed CEO algorithm is further verified by comparing with the benchmark using the genetic algorithm (GA).

Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method.

FAS-RIS Communication: Model, Analysis, and Optimization

This correspondence investigates the novel fluid antenna system (FAS) technology, combining with reconfigurable intelligent surface (RIS) for wireless communications, where a base station (BS) communicates with a FAS-enabled user with the assistance of a RIS. To analyze this technology, we derive the outage probability based on the block-diagonal matrix approximation (BDMA) model. With this, we obtain the upper bound, lower bound, and asymptotic approximation of the outage probability to gain more insights. Moreover, we design the phase shift matrix of the RIS in order to minimize the system outage probability. Simulation results confirm the accuracy of our approximations and that the proposed schemes outperform benchmarks significantly.

FAS-RIS: A Block-Correlation Model Analysis

In this correspondence, we analyze the performance of a reconfigurable intelligent surface (RIS)-aided communication system that involves a fluid antenna system (FAS)-enabled receiver. By applying the central limit theorem (CLT), we derive approximate expressions for the system outage probability when the RIS has a large number of elements. Also, we adopt the block-correlation channel model to simplify the outage probability expressions, reducing the computational complexity and shedding light on the impact of the number of ports. Numerical results validate the effectiveness of our analysis, especially in scenarios with a large number of RIS elements.